Publications, Activities, and Awards
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Accelerated discovery of perovskites and prediction of band gaps using machine-learning methods
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Accelerating the Discovery of Materials with Machine Learning: Potential Roadblocks and How to Overcome Them
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Accelerating the Discovery of Materials with Machine Learning: Potential Roadblocks and How to Overcome Them
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Accelerating the Discovery of Materials: Machine-Learning Approach
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Accelerating the Discovery of Solid State Materials with Machine-Learning Approaches
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Accelerating the Discovery of Solid State Materials: From Traditional to Machine-Learning Approaches
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Accelerating the Discovery of Solid State Materials: From Traditional to Machine-Learning Approaches
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Accelerating the Discovery of Solid State Materials: From Traditional to Machine-Learning Approaches
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Accelerating the discovery of solid state materials: From traditional to machine- learning approaches
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Accelerating the Discovery of Solid State Materials: From Traditional to Machine-Learning Approaches
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Accelerating the Discovery of Solid State Materials: From Traditional to Machine-Learning Approaches
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Accelerating the Discovery of Solid State Materials: From Traditional to Machine-Learning Approaches
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Accelerating the Discovery of Solid State Materials: From Traditional to Machine-Learning Approaches
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Accelerating the Discovery of Solid State Materials: From Traditional to Machine-Learning Approaches
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Accelerating the Discovery of Solid State Materials: From Traditional to Machine-Learning Approaches
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Accelerating the Discovery of Solid State Materials: From Traditional to Machine-Learning Approachs
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Alkaline Earth Metal-Organic Frameworks with Tailorable Ion Release: A Path for Supporting Biomineralization
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Classification of Half-Heusler Compounds through Machine Learning Approaches
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Classification of Half-Heusler Compounds through Machine-Learning Approaches
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Computational workshop
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Crystallography in Chemistry and Materials Science
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Design of Experiments and Machine Learning-Assisted Organic Solar Cell Efficiency Optimization
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Discovery of Intermetallic Compounds from Traditional to Machine-Learning Approaches
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Discovery of Noncentrosymmetric Ternary Compounds from Elemental Composition: A Machine-Learning Approach
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Discovery of ternary noncentrosymmetric compounds: A machine-learning approach with experimental proof
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Disentangling Structural Confusion through Machine Learning: Structure Prediction and Polymorphism of Equiatomic Ternary Phases ABC
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Effectively Exploring Parameter Space: Design of Experiments and Machine Learning-assisted Organic Solar Cell Efficiency Optimization
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Excellence in Undergraduate Teaching 2017
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Excellence in Undergraduate Teaching 2018
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Exploring the colours of gold alloys with machine learning
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Faculty of Science Students' Choice Honour Roll
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Future Energy Systems Research Symposium
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Hexagonal Double Perovskite Cs2AgCrCl6
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High-Throughput Approaches for Discovering Thermoelectric Materials
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How large is an atom?
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How to look for compounds
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How to look for compounds
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How to look for compounds
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How To Optimize Materials and Devices via Design of Experiments and Machine Learning: Demonstration Using Organic Photovoltaics
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In Search of Coloured Intermetallics
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Influence of hidden halogen mobility on local structure of CsSn (Cl1-xBrx)3 mixed-halide perovskites by solid-state NMR
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Introduction to Machine Learning: A Practical Workshop
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Invited talk: ACS national meeting, Boston
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Machine Learning and Models: How we find optimal materials for Solar and CCS technologies
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Machine-learning predictions of half-Heusler structures
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Not Just Par for the Course: 73 Quaternary Germanides RE4M2XGe4 (RE = La–Nd, Sm, Gd–Tm, Lu; M = Mn–Ni; X = Ag, Cd) and the Search for Intermetallics with Low Thermal Conductivity
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Prediction of Novel Compounds and Rapid Property Screening through a Machine Learning Approach
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Quarternary Rare-earth Transition-Metal Germanides: RE4M2CdGe4 and RE4M2AgGe4 (RE=La-SM, Gd-Lu, M=Mn-Ni)
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Quaternary rare-earth sulfides RE3M0.5M'S7 (M = Zn, Cd; M' = Si, Ge)
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Quaternary Rare-Earth Transition-Metal Germanides RE4M2CdGe4 and RE4M2AgGe4 (RE = La–Sm, Gd–Tm, Lu; M = Mn–Ni)
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Rare-earth transition-metal oxyselenides
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Searching for Missing Binary Equiatomic Phases: Complex Crystal Chemistry in the Hf–In System
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Solving the Colouring Problem in Half-Heusler Structures: Machine-Learning Predictions and Experimental Validation
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Structure and Luminescence Properties of Rare-Earth Chalcohalides RE3Ge2Ch8X (Ch = S, Se; X = Cl, Br, I)
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Synthesis, structure, and properties of rare-earth germanium sulfide iodides RE3Ge2S8I (RE = La, Ce, Pr)
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Ternary and Quaternary Rare-Earth Transition-Metal Germanides
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Ternary and quaternary rare‐earth germanides: discovery of intermetallic compounds from traditional to machine‐learning approaches
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Ternary Germanides in Ce-M-Ge System (M=Rh, Co)
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Ternary Germanides in the Ce–M–Ge (M = Rh, Co) Systems
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Thermoelectric properties of inverse perovskites A3TtO (A = Mg, Ca; Tt = Si, Ge): Computational and experimental investigations
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USchool: Materials and Informatics
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X-ray diffraction short course